Course Syllabus
Obiettivi formativi
L’obiettivo del corso è quello di approfondire e analizzare alcuni sviluppi teorici ed empirici nell'ambito del portfolio management, focalizzando in particolare l’attenzione sul tema dell’asset allocation tattica e sui principali modelli quantitativi di stock selection.
In tal senso, parte delle lezioni saranno di carattere applicativo/informatico, basate sull'utilizzo dei software R e Python.
Contenuti sintetici
Il corso si concentra su due macro aree di argomenti. Una prima parte focalizzata sui modelli più avanzati di teoria di portafoglio, quindi modelli di Asset Allocation Strategico/Tattica. Il focus in questa prima parte sarà rivolto più alle applicazioni empiriche dei modelli e le tematiche più tecniche sui dati coinvolti nello sviluppo degli stessi. Nella seconda parte il corso si concentra su tematiche più specifiche legate all'implementazione di strategie di investimento attive, chiarendo il rapporto con l'ipotesi di efficienza del mercato e la rilevanza dei dati e della loro qualità al fine di ottenere risultati di qualità.
Programma esteso
Modern Portfolio Theory
- a) Introduction to asset management
- b) The properties of financial assets, returns properties, volatility and correlations
- c) The portfolio choice, the Markowitz model
- d) The Implementation of the Markowitz model, the efficient frontier
- e) From Markowitz to an equilibrium approtch the CAPM
- f) The new Efficient Frontier the Capital Market Line (CML)
- g) The Market Portfolio in the CML and the passive management
- h) The Security Market Line and the Beta in the CAPM
- i) Active vs Passive management, total risk vs systematic and specific risk
- l) The Markowitz model drawbacks, instability and low diversification
Improving Asset Allocation
- a) The possible solutions to Markowitz limits
- b) The contrained efficient frontier
- c) A more objective solution: the resampled frontier
- d) The steps to implement the resampling
- e) The cons and critiques of the resampling
- f) An alternative approach the Black and Littermann (BL) model
- g) The BL implementaion, equilibrium returns, the implementation of views
- h) The market model and the jensen's alpha as the ability to overperform the market
One-off topics in Portfolio Management
- a) Basic priciples of stock valuations: analysts consensus
- b) Basic priciples of stock valuations: key financial items, dividends, market orders and liquidity
- c) Active Funds: The case of hedge funds
- d) Hedge funds popular strategies
- e) Market effinciency and links with extraperformance (Alpha)
- f) The pairs trading strategy based on cointegration tests
- g) Implementation of the pairs trading startegy
- h) The momentum strategy, cross sectional, time series and residual version
- I) The dangerous biases that arise when working with historical datasets: look-ahead bias and survivorship bias
Prerequisiti
Non ci sono prerequisiti formalmente richiesti per il corso, saranno però utili conoscenze base di teoria finanziaria.
Ci si attende inoltre che gli studenti conoscano i concetti fondamentali di statistica e in particolare quelli connessi ai modelli di regressione lineare multipla. Saranno anche dati per scontati i concetti base di algebra matriciale.
Metodi didattici
Il corso viene impartito in maniera tradizionale, basato quindi su didattica frontale. Prevede lo svolgimento di applicazioni e sviluppo di modelli con l'ausilio del linguaggio di programmazione R o Python. Lo sviluppo di modelli, di cui alcuni esempi sono il modello di Black and Littermann e il Ricampionamento della frontiera efficiente, risulta prodromico all' Assignment che viene erogato al termine del corso e che rappresenta un'opportunità concreta per gli studenti di mettere a frutto e consolidare le conoscenze sviluppate durante l'insegnamento.
Modalità di verifica dell'apprendimento
L'apprendimento verrà verificato attraverso:
1. Una presentazione orale dell'Assignment in gruppi di 2-3 studenti che sarà proposto durante il corso, con discussione critica dei risultati.
2. Un esame orale.
Il risultato finale sarà la media delle due componenti.
Questa modalità di verifica dell'apprendimento è motivata dall'obiettivo di mettere gli studenti nelle condizioni operative tipiche dell'attività lavorativa e di farne in particolare emergere le abilità soft (organizzative, comunicative, creative...).
Testi di riferimento
Il materiale del corso si basa in parte sul testo:
- Ludwig B Chincarini, Daehwan Kim, 2006, Quantitative Equity Portfolio Management, McGraw-Hill Library of Investment and Finance. I capitoli del testo rilevanti vanno dal cap.1 al 7.
Il manuale copre all’incirca il 30% degli argomenti trattati durante il corso. I restanti argomenti saranno basati su set di slides utilizzate durante il corso, risorse web sulla programmazione delle strategie in R e Python, e articoli da riviste scientifiche:
- Scherer, B., 2002, Portfolio Resampling: Review and Critique, Financial Analysts Journal, 58(6), pp. 98-109.
- Drobetz, W., 2001, How to Avoid the Pitfalls in Portfolio Optimization? Putting the Black-Litterman Approach at Work, Swiss Society for Financial Market Research, 15(1), pp. 59-75.
Tutte le risorse saranno rese disponibili agli studenti.
Periodo di erogazione dell'insegnamento
Secondo semestre
Lingua di insegnamento
Italiano
Learning objectives
The objective of the course is to deepen and analyze some theoretical and empirical developments in portfolio management, focusing in particular on tactical asset allocation and the main quantitative models of stock selection.
In this perspective, some of the classes will be of an applicative/informatics nature, based on the use of the software R and Python.
Contents
The course focuses on two macro areas of topics. A first part focused on more advanced portfolio theory models, i.e. Strategic/Tactical Asset Allocation models. The focus in this first part will be more on the empirical applications of the models and the more technical data issues involved in the development of the models. In the second part the course focuses on more specific topics, related to the implementation of active investment strategies, clarifying the relationship with the market efficiency hypothesis, the relevance of data and its quality for building reliable investment strategies.
Detailed program
Modern Portfolio Theory
- a) Introduction to asset management
- b) The properties of financial assets, returns properties, volatility and correlations
- c) The portfolio choice, the Markowitz model
- d) The Implementation of the Markowitz model, the efficient frontier
- e) From Markowitz to an equilibrium approtch the CAPM
- f) The new Efficient Frontier the Capital Market Line (CML)
- g) The Market Portfolio in the CML and the passive management
- h) The Security Market Line and the Beta in the CAPM
- i) Active vs Passive management, total risk vs systematic and specific risk
- l) The Markowitz model drawbacks, instability and low diversification
Improving Asset Allocation
- a) The possible solutions to Markowitz limits
- b) The contrained efficient frontier
- c) A more objective solution: the resampled frontier
- d) The steps to implement the resampling
- e) The cons and critiques of the resampling
- f) An alternative approach the Black and Littermann (BL) model
- g) The BL implementaion, equilibrium returns, the implementation of views
- h) The market model and the jensen's alpha as the ability to overperform the market
One-off topics in Portfolio Management
- a) Basic priciples of stock valuations: analysts consensus
- b) Basic priciples of stock valuations: key financial items, dividends, market orders and liquidity
- c) Active Funds: The case of hedge funds
- d) Hedge funds popular strategies
- e) Market effinciency and links with extraperformance (Alpha)
- f) The pairs trading strategy based on cointegration tests
- g) Implementation of the pairs trading startegy
- h) The momentum strategy, cross sectional, time series and residual version
- I) The dangerous biases that arise when working with historical datasets: look-ahead bias and survivorship bias
Prerequisites
There are no formal prerequisites for the course, but basic knowledge of financial theory will be useful.
Students are also expected to know the basic concepts of statistics and in particular those related to multiple linear regression models. Basic concepts of matrix algebra will also be given for granted.
Teaching methods
The course is taught in a traditional way, therefore based on frontal teaching. It provides for the development of applications and models in the computer lab with the help of the R or Python programming language. The implementation of models, of which some examples are the Black and Littermann model and the efficient frontier resampling, is prodromic to the Assignment that is delivered at the end of the course and represents a concrete opportunity for students to exploit and consolidate the knowledge developed during the course.
Assessment methods
The learning will be tested through:
1. An oral presentation of the voluntary Assignment, proposed during the course, in groups of 2-3 students, with critical discussion of the results.
2. An oral assessment.
The final result will be the average of the two parts.
This method of assessing learning is motivated by the objective of putting the students in the operational conditions typical of work activity and to bring out in particular their soft skills (organisational, communicative, creative...).
Textbooks and Reading Materials
The course content is based in part on the book:
- Ludwig B Chincarini, Daehwan Kim, 2006, Quantitative Equity Portfolio Management, McGraw-Hill Library of Investment and Finance. The relevant chapters of the text range from ch.1 to 7.
The textbook covers approximately 30% of the course topics. The remaining topics are taught using sets of slides, web resouces on implementation of strategies in R and Python, and some papers:
- Scherer, B., 2002, Portfolio Resampling: Review and Critique, Financial Analysts Journal, 58(6), pp. 98-109.
- Drobetz, W., 2001, How to Avoid the Pitfalls in Portfolio Optimization? Putting the Black-Litterman Approach at Work, Swiss Society for Financial Market Research, 15(1), pp. 59-75.
All the resouces wiil be made available to students
Semester
Second semester
Teaching language
Italian
Key information
Staff
-
Gianfranco Forte